Automated Analysis of Alignment in Long-Leg Radiographs by Using a Fully Automated Support System Based on Artificial Intelligence - Schock, Justus (Radiol Artif Intell, 2020)
PURPOSE: To develop and validate a deep learning–based method for automatic quantitative analysis of lower-extremity alignment. MATERIALS AND METHODS: In this retrospective study, bilateral long-leg radiographs (LLRs) from 255 patients that were obtained between January and September of 2018 were included. For training data (n = 109), a U-Net convolutional neural network was trained to segment the femur and tibia versus manual segmentation. For validation data (n = 40), model parameters were optimized. Following identification of anatomic landmarks, anatomic and mechanical axes were identified and used to quantify alignment through the hip-knee-ankle angle (HKAA) and femoral anatomic-mechanical angle (AMA). For testing data (n = 106), algorithm-based angle measurements were compared with reference measurements by two radiologists. Angles and time for 30 random radiographs were compared by using repeated-measures analysis of variance and one-way analysis of variance, whereas correlations were quantified by using Pearson r and intraclass correlation coefficients. RESULTS: Bilateral LLRs of 255 patients (mean age, 26 years ± 23 [standard deviation]; range, 0–88 years; 157 male patients) were included. Mean Sørensen-Dice coefficients for segmentation were 0.97 ± 0.09 for the femur and 0.96 ± 0.11 for the tibia. Mean HKAAs and AMAs as measured by the readers and the algorithm ranged from 0.05° to 0.11° (P = .5) and from 4.82° to 5.43° (P < .001). Interreader correlation coefficients ranged from 0.918 to 0.995 (r range, P < .001), and agreement was almost perfect (intraclass correlation coefficient range, 0.87–0.99). Automatic analysis was faster than the two radiologists’ manual measurements (3 vs 36 vs 35 seconds, P < .001). CONCLUSION: Fully automated analysis of LLRs yielded accurate results across a wide range of clinical and pathologic indications and is fast enough to enhance and accelerate clinical workflows. Supplemental material is available for this article. © RSNA, 2020 See also commentary by Andreisek in this issue.